A common data-processing problem is the retrieval or counting of objects in a data store whose extent includes a given point or extent. The extents can be one-dimensional, such as temporal durations, or higher-dimensional, such as areas, volumes, etc. The problem is an old one and has been approached in many ways: bounding lists, multi-dimensional indexing, quad-trees, various hierarchical trees such as interval trees and R-trees, and simplex range searching. While the problem has been solved for non-distributed data stores, in the age of big data and distributed stores this is no longer the case. Hierarchical trees are neither well suited for, as an example, key-value databases nor Hadoop disk dumps. A need exists for a method that can improve data search and retrieval for applications such as distributed data stores.